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          分析结果不一样
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             分析结果不一样
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               2009年5月24日 上午7:07
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              <p>
               分别用SPSS和R软件做典型相关分析，输出的却不一样。
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               2009年5月26日 下午2:09
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              <p>
               可以说得稍微具体些，看过这本书的人也不一定记得这个问题
              </p>
              <p>
               R里可以直接看到函数内容，看到分析是如何实现的，你可以先自己看一下
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               2009年5月27日 上午2:18
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              <p>
               书中给出了电视节目评价的例子，用来分析两组变量对节目观点之间的关系
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               2009年5月31日 上午11:59
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               没人帮吗
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               2009年5月31日 下午5:13
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               我这里只有Ｒ没有SPSS
               <br/>
               我看了一下吴老师的ＰＰＴ，里面给的SPSS代码没有标准化命令啊
               <br/>
               Ｒ里的　cancor　好像也没有默认标准化吧，你用　scale　标准化后再比较一下
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               2009年6月1日 上午3:57
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               不是这个原因。SPSS在做典型相关分析时同时给出数据标准化和数据未标准化两种情况下的综合变量（典型变量）系数。刚才我又试了试，把数据标准化后，再分别用spss和r软件做，两个软件给出的结果还是不一样。
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               2009年6月1日 上午5:43
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               不知你是用什么方法标准化的。也许是两个软件标准化方法有区别？你可以搜一下spss的帮助，看它是怎么标准化的
              </p>
              <pre class="highlight ">test&lt;-scale(test)
ca&lt;-cancor(test[,1:3],test[,4:6])
</pre>
              <p>
               大多数情况下相信Ｒ就行，spss可以无视
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               2009年6月1日 下午2:49
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               我在两个软件上用的数据全部是经过R软件标准化的，也就是说，是经过R软件标准化后的数据再进入SPSS软件进行典型相关分析的，而且可以得到验证（这时SPSS输出的典型变量的标准化系数与非标准化系数是完全一样的）。
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               2009年6月1日 下午4:17
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               lanfeng
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              <div class="bbp-author-role">
               版主
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              <p>
               你这问题让人很为难，数据程序什么都没有
               <br/>
               你至少把程序贴出来，把部分数据贴出来
              </p>
             </div>
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            <!-- .reply -->
            <div class="bbp-reply-header" id="post-271791">
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              <span class="bbp-reply-post-date">
               2009年6月3日 上午1:58
              </span>
              <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/15384/#post-271791">
               10 楼
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               jhfu
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               普通会员
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              <p>
               1、程序语句
               <br/>
               MANOVA led hed net WITH arti com man
               <br/>
               /DISCRIM ALL ALPHA(1)
               <br/>
               /PRINT =SIGNIF(EIGEN DIM).
               <br/>
               2、输出结果* * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e * * * * * * * * * * * * * * * * *
               <br/>
               The default error term in MANOVA has been changed from WITHIN CELLS to
               <br/>
               WITHIN+RESIDUAL.  Note that these are the same for all full factorial designs.
               <br/>
               * * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e — Design   1 * * * * * * * * * * * * * * * * *
               <br/>
               EFFECT .. WITHIN CELLS Regression
               <br/>
               Multivariate Tests of Significance (S = 3, M = -1/2, N = 11 )
              </p>
              <p>
               Test Name             Value        Approx. F       Hypoth. DF         Error DF        Sig. of F
              </p>
              <p>
               Pillais               2.30495         28.74054             9.00            78.00             .000
               <br/>
               Hotellings       119.44882     300.83406           9.00            68.00             .000
               <br/>
               Wilks                  .00050        141.58046             9.00            58.56             .000
               <br/>
               Roys                   .99090
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Eigenvalues and Canonical Correlations
              </p>
              <p>
               Root No.       Eigenvalue           Pct.      Cum. Pct.     Canon Cor.        Sq. Cor
              </p>
              <p>
               1        108.91116       91.17810       91.17810         .99544         .99090
               <br/>
               2          9.85364         8.24926         99.42736          .95282         .90787
               <br/>
               3           .68401          .57264           100.00000         .63732         .40618
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Dimension Reduction Analysis
              </p>
              <p>
               Roots              Wilks L.                F       Hypoth. DF         Error DF        Sig. of F
              </p>
              <p>
               1 TO 3               .00050        141.58046             9.00            58.56             .000
               <br/>
               2 TO 3               .05471         40.94049             4.00            50.00             .000
               <br/>
               3 TO 3               .59382         17.78432             1.00            26.00             .000
              </p>
              <p>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               EFFECT .. WITHIN CELLS Regression (Cont.)
               <br/>
               Univariate F-tests with (3,26) D. F.
              </p>
              <p>
               Variable       Sq. Mul. R     Adj. R-sq.     Hypoth. MS       Error MS              F      Sig. of F
              </p>
              <p>
               led                .89994         .88839        8.69941         .11161       77.94711           .000
               <br/>
               hed                .98818         .98682        9.55243         .01318      724.68163           .000
               <br/>
               net                .77631         .75050        7.50434         .24950       30.07759           .000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Raw canonical coefficients for DEPENDENT variables
               <br/>
               Function No.
               <br/>
               Variable                  1                2                3
              </p>
              <p>
               led                  .14887           .78575         -1.21198
               <br/>
               hed                  .97696          -.38311          -.15951
               <br/>
               net                 -.05201           .31163          1.46710
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Standardized canonical coefficients for DEPENDENT variables
               <br/>
               Function No.
               <br/>
               Variable                  1                2                3
              </p>
              <p>
               led                  .14887           .78575         -1.21198
               <br/>
               hed                  .97696          -.38311          -.15951
               <br/>
               net                 -.05201           .31163          1.46710
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Correlations between DEPENDENT and canonical variables
               <br/>
               Function No.
              </p>
              <p>
               Variable                  1                2                3
              </p>
              <p>
               led                  .33252           .92484          -.18466
               <br/>
               hed                  .99329          -.10084           .05663
               <br/>
               net                  .38269           .75305           .53522
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Variance in dependent variables explained by canonical variables
              </p>
              <p>
               CAN. VAR.       Pct Var DEP      Cum Pct DEP      Pct Var COV      Cum Pct COV
              </p>
              <p>
               1           41.45484         41.45484         41.07767         41.07767
               <br/>
               2           47.75277         89.20761         43.35307         84.43074
               <br/>
               3           10.79239        100.00000          4.38365         88.81440
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Raw canonical coefficients for COVARIATES
               <br/>
               Function No.
              </p>
              <p>
               COVARIATE                 1                2                3
              </p>
              <p>
               arti                 .85751          -.91113         -1.98252
               <br/>
               com                  .01930          1.04627         -1.11428
               <br/>
               man                  .14539           .33714          2.83316
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Standardized canonical coefficients for COVARIATES
               <br/>
               CAN. VAR.
               <br/>
               COVARIATE                 1                2                3
              </p>
              <p>
               arti                 .85751          -.91113         -1.98252
               <br/>
               com                  .01930          1.04627         -1.11428
               <br/>
               man                  .14539           .33714          2.83316
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Correlations between COVARIATES and canonical variables
               <br/>
               CAN. VAR.
              </p>
              <p>
               Covariate                 1                2                3
              </p>
              <p>
               arti                 .99696          -.06468          -.04346
               <br/>
               com                  .57103           .81123          -.12584
               <br/>
               man                  .92221           .27380           .27306
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Variance in covariates explained by canonical variables
              </p>
              <p>
               CAN. VAR.       Pct Var DEP      Cum Pct DEP      Pct Var COV      Cum Pct COV
              </p>
              <p>
               1           71.69100         71.69100         72.34925         72.34925
               <br/>
               2           22.31042         94.00142         24.57460         96.92385
               <br/>
               3            1.24947         95.25089          3.07615        100.00000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Regression analysis for WITHIN CELLS error term
               <br/>
               — Individual Univariate .9500 confidence intervals
               <br/>
               Dependent variable .. led
              </p>
              <p>
               COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
              </p>
              <p>
               arti         -.2857414421   -.2857414421         .14543       -1.96476           .060        -.58468         .01320
               <br/>
               com          1.0595057740   1.0595057740         .09483       11.17272           .000         .86458        1.25443
               <br/>
               man           .0117817825    .0117817825         .17723         .06648           .948        -.35252         .37608
               <br/>
               Dependent variable .. hed
              </p>
              <p>
               COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
              </p>
              <p>
               arti          .8638568439    .8638568439         .04998       17.28381           .000         .76112         .96659
               <br/>
               com          -.1216608663   -.1216608663         .03259       -3.73309           .001        -.18865        -.05467
               <br/>
               man           .2136198867    .2136198867         .06091        3.50728           .002         .08842         .33882
               <br/>
               Dependent variable .. net
              </p>
              <p>
               COVARIATE               B           Beta      Std. Err.        t-Value      Sig. of t     Lower -95%     CL- Upper
              </p>
              <p>
               arti        -1.0033492783  -1.0033492783         .21745       -4.61422           .000       -1.45032        -.55638
               <br/>
               com           .3779792532    .3779792532         .14179        2.66584           .013         .08653         .66943
               <br/>
               man          1.2637082710   1.2637082710         .26499        4.76896           .000         .71902        1.80840
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               * * * * * * * * * * * * * * * * * A n a l y s i s   o f   V a r i a n c e — Design   1 * * * * * * * * * * * * * * * * *
               <br/>
               EFFECT .. CONSTANT
               <br/>
               Multivariate Tests of Significance (S = 1, M = 1/2, N = 11 )
              </p>
              <p>
               Test Name             Value          Exact F       Hypoth. DF         Error DF        Sig. of F
              </p>
              <p>
               Pillais                .00000           .00000             3.00            24.00            1.000
               <br/>
               Hotellings             .00000           .00000             3.00            24.00            1.000
               <br/>
               Wilks                 1.00000           .00000             3.00            24.00            1.000
               <br/>
               Roys                   .00000
               <br/>
               Note.. F statistics are exact.
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Eigenvalues and Canonical Correlations
              </p>
              <p>
               Root No.       Eigenvalue           Pct.      Cum. Pct.     Canon Cor.
              </p>
              <p>
               1           .00000            INF      100.00000         .00000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               EFFECT .. CONSTANT (Cont.)
               <br/>
               Univariate F-tests with (1,26) D. F.
              </p>
              <p>
               Variable         Hypoth. SS         Error SS       Hypoth. MS         Error MS                F        Sig. of F
              </p>
              <p>
               led                  .00000          2.90177           .00000           .11161           .00000            1.000
               <br/>
               hed                  .00000           .34272           .00000           .01318           .00000            1.000
               <br/>
               net                  .00000          6.48698           .00000           .24950           .                  .
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               EFFECT .. CONSTANT (Cont.)
               <br/>
               Raw discriminant function coefficients
               <br/>
               Function No.
              </p>
              <p>
               Variable                  1
              </p>
              <p>
               led                -3.15310
               <br/>
               hed                -4.40979
               <br/>
               net                  .00000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Standardized discriminant function coefficients
               <br/>
               Function No.
              </p>
              <p>
               Variable                  1
              </p>
              <p>
               led                -1.05337
               <br/>
               hed                 -.50629
               <br/>
               net                  .00000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Estimates of effects for canonical variables
               <br/>
               Canonical Variable
              </p>
              <p>
               Parameter                1
              </p>
              <p>
               1             .00000
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
               <br/>
               Correlations between DEPENDENT and canonical variables
               <br/>
               Canonical Variable
              </p>
              <p>
               Variable                  1
              </p>
              <p>
               led                 -.87968
               <br/>
               hed                 -.14491
               <br/>
               net                 -.18912
               <br/>
               – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
              </p>
             </div>
             <!-- .bbp-reply-content -->
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            <!-- .reply -->
            <div class="bbp-reply-header" id="post-271792">
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              <span class="bbp-reply-post-date">
               2009年6月3日 上午2:08
              </span>
              <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/15384/#post-271792">
               11 楼
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               jhfu
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              <p>
               这是R软件的程序语句和输出结果：
              </p>
              <p>
               1、程序语句
               <br/>
               &gt; data=read.csv("tv标准化.csv",head=TRUE)
               <br/>
               &gt; summary(data)
               <br/>
               &gt; cancor=cancor(data[,2:4],data[,5:7],xcenter=TRUE,ycenter=TRUE);cancor
               <br/>
               2、输出结果
               <br/>
               $cor
               <br/>
               [1] 0.9954405 0.9528195 0.6373226
              </p>
              <p>
               $xcoef
               <br/>
               [,1]        [,2]       [,3]
               <br/>
               led  0.027644163  0.14591001  0.2250583
               <br/>
               hed  0.181416647 -0.07114094  0.0296210
               <br/>
               net -0.009658358  0.05786891 -0.2724343
              </p>
              <p>
               $ycoef            [,1]        [,2]       [,3]
               <br/>
               arti 0.159235675 -0.16919348  0.3681442
               <br/>
               com  0.003583524  0.19428738  0.2069161
               <br/>
               man  0.026997651  0.06260513 -0.5261045
              </p>
              <p>
               $xcenter
               <br/>
               led           hed           net
               <br/>
               6.666668e-11  3.333333e-11 -7.979728e-18
              </p>
              <p>
               $ycenter
               <br/>
               arti           com           man
               <br/>
               3.333334e-11 -3.333334e-11  1.780983e-17
              </p>
             </div>
             <!-- .bbp-reply-content -->
            </div>
            <!-- .reply -->
            <div class="bbp-reply-header" id="post-271795">
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              <span class="bbp-reply-post-date">
               2009年6月3日 上午2:29
              </span>
              <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/15384/#post-271795">
               12 楼
              </a>
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              </span>
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              </a>
              <br/>
              <a class="bbp-author-name" href="http://cos.name/cn/profile/58534/" rel="nofollow" title="查看jhfu的档案">
               jhfu
              </a>
              <br/>
              <div class="bbp-author-role">
               普通会员
              </div>
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             <!-- .bbp-reply-author -->
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              <p>
               "序号""led" "hed" "net" "arti" "com" "man"
               <br/>
               "1" 1.63803569955174 -0.188929954706770 1.17587534421617 -0.169090275912417 1.74660372983861 0.462384851401061
               <br/>
               "2" 2.22091336636303 0.820868079070793 1.74094129910267 0.998967551114146 2.00859428931440 1.20550336258134
               <br/>
               "3" -0.55896473689083 -0.872986687265764 -1.8512636998186 -0.703059568267417 -1.26628770413299 -1.10642089442397
               <br/>
               "4" -1.99374053211863 -0.970709077631334 0.00538158052272847 -1.17028269907804 -1.83393391633054 -0.89999908576278
               <br/>
               "5" -0.200270788083880 -0.188929954706770 -0.0349802733977352 -0.302582599001167 0.0436650932459651 -0.0743118511180276
               <br/>
               "6" -1.27635263450473 -0.547245386047195 -1.4072833066935 -0.469448002862104 -0.785971678427372 -1.14770525615621
               <br/>
               "7" -0.603801480491699 0.95116459955822 -0.317513250840981 0.898848308797583 -0.480316025705616 0.751375383526724
               <br/>
               "8" 0.875811058336969 -0.579819516169052 1.17587534421617 -0.536194164406479 0.742306585181407 -0.322018021511453
               <br/>
               "9" -0.424454506088224 1.60264720199536 -0.801855497886545 1.69980224733008 0.480316025705616 1.08165027738463
               <br/>
               "10" -1.05216891650039 -1.13357972824062 -0.64040808220469 -1.33714810293898 -1.22262261088702 -1.60183323521082
               <br/>
               "11" 0.068749673521332 0.625423298339652 -0.721131790045617 0.665236743392271 -0.218325466229826 0.503669213133299
               <br/>
               "12" 0.606790596731757 1.21175764053307 0.933704220693393 1.29932527806383 1.00429714465720 1.45320953297476
               <br/>
               "13" -0.469291249689093 1.01631285980193 0.0457434344431921 1.06571371265852 0.0873301864919303 0.338531766204348
               <br/>
               "14" 1.27934175074479 -0.28665234507234 0.651171243250147 -0.202463356684604 0.960632051411233 -0.0743118511180276
               <br/>
               "15" 0.292933391525676 0.00651482602437146 -0.0753421273181989 -0.00222487205147928 -0.0436650932459651 0.0495412340786852
               <br/>
               "16" -0.469291249689093 1.01631285980193 0.610809389329683 0.93222138956977 -0.0436650932459651 0.875228468723437
               <br/>
               "17" 0.696464083933494 -1.42674689933733 -0.115703981238663 -1.23702886062242 0.611311305443512 -0.776146000566067
               <br/>
               "18" -0.962495429298649 -1.23130211860619 -1.00366476748886 -1.20365577985023 -1.30995279737895 -1.02385217095949
               <br/>
               "19" 0.0239129299204632 -0.547245386047195 0.489723827568292 -0.836551891356167 -0.174660372983861 -0.0743118511180276
               <br/>
               "20" 0.875811058336969 1.60264720199536 0.530085681488756 1.63305608578571 1.22262261088702 1.61834697990371
               <br/>
               "21" 0.248096647924807 1.63522133211721 0.893342366772929 1.63305608578571 0.305655652721756 1.24678772431358
               <br/>
               "22" -0.603801480491699 -1.23130211860619 -2.05307296942092 -1.10353653753367 -1.17895751764106 -2.26238302292662
               <br/>
               "23" 1.23450500714392 -1.00328320775319 0.207190850125047 -0.703059568267417 0.654976398689477 -0.239449298046978
               <br/>
               "24" 0.786137571135232 -0.514671255925338 1.57949388342081 -0.469448002862104 0.261990559475791 0.0495412340786852
               <br/>
               "25" -0.200270788083880 1.24433177065493 -0.398236958681908 1.23257911651946 0.611311305443512 0.8339441069912
               <br/>
               "26" 0.517117109530019 0.755719818827079 0.287914557965974 0.498371339531333 0.392985839213686 0.627522298330012
               <br/>
               "27" -0.379617762487355 0.462552647730367 0.731894951091074 0.231386693353833 -0.130995279737895 0.668806660062249
               <br/>
               "28" -1.94890378851776 -1.42674689933733 -1.73017813805721 -1.43726734525554 -2.09592447580633 -1.93210812906872
               <br/>
               "29" -0.962495429298649 0.136811346511799 -0.842217351807008 0.0978943702650832 -0.960632051411233 -0.0330274893857900
               <br/>
               "30" 0.741300827534363 -0.938134947509477 0.933704220693393 -1.00341729521710 0.611311305443512 -0.198164936314740
              </p>
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              <span class="bbp-reply-post-date">
               2009年6月3日 上午6:07
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               13 楼
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              <pre class="highlight ">xx=read.table('xxx.txt')
xc=cancor(xx[2:4],xx[5:7])

 xc$xcoef[,1]/c(.14887,  .97696 ,-.05201   )

xc$ycoef[,1]/c(  .85751 ,   .01930    ,   .14539 )

 t(c(  .85751 ,   .01930    ,   .14539 ))%*%cov(xx[5:7])%*%c(  .85751 ,   .01930    ,   .14539 )

</pre>
              <p>
               其实两个软件算出来的系数也是一样的，只是相差了一个定的倍数
               <br/>
               SPSS里面的系数是我们理论上常用的系数，因为满足第五行代码乘积为１
              </p>
              <p>
               在Ｒ里直接输入　cancor　可以看到函数内容，很短，你可以看一下Ｒ是如何计算的
              </p>
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               2009年6月3日 上午6:21
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               14 楼
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              <p>
               如果你想用Ｒ也得到标准的系数可以这样
              </p>
              <pre class="highlight ">install.packages('CCA')
library(CCA)
cc(xx[2:4],xx[5:7])
</pre>
              <p>
               你会发现 cc 这个 函数很齐全
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               2009年6月25日 上午2:58
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